This approach directly addresses the core requirement of creating semantic embeddings in a fully managed and scalable manner. Amazon Titan Text Embeddings, available through the serverless Amazon Bedrock API, is specifically designed to convert textual data into high-quality vector representations. These embeddings capture the contextual and semantic meaning of the text. Storing these vectors in Amazon OpenSearch Service (with its k-NN search capability) creates a scalable vector store. This architecture is the foundational pattern for Retrieval-Augmented Generation (RAG) and integrates natively with agents for Amazon Bedrock, which can query this vector store to retrieve relevant context for downstream tasks.

A. Amazon Kendra is a high-level intelligent search service. While it performs semantic search internally and can serve as a knowledge base for Bedrock agents, it abstracts away the direct creation and management of embeddings.

B. Amazon SageMaker Data Wrangler is a service for data preparation and feature engineering for traditional machine learning models, not for generating the semantic embeddings required for RAG workflows.

C. While SageMaker JumpStart can deploy embedding models, it requires managing a dedicated endpoint. Using the serverless Amazon Bedrock API for Titan Embeddings is a more "fully managed" solution that aligns better with the requirements.

1. Amazon Bedrock User Guide - Amazon Titan models: "Amazon Titan Text Embeddings G1 - The Titan Text Embeddings model translates text inputs (words, phrases, or large units of text) into a numerical representation (known as embeddings) that can be used to power use cases like search, personalization, and clustering based on semantic similarity." (Source: AWS Documentation, Amazon Bedrock User Guide, Section: "Amazon Titan models").

2. Amazon Bedrock User Guide - Knowledge bases for Amazon Bedrock: "When you create a knowledge base, Amazon Bedrock converts your documents into embeddings... and stores the embeddings in your vector database... You can choose from a variety of vector databases to store the vectors for your knowledge base, including Amazon OpenSearch Service Serverless..." This document explicitly outlines the architecture described in the correct answer. (Source: AWS Documentation, Amazon Bedrock User Guide, Section: "Knowledge bases for Amazon Bedrock").

3. Amazon Kendra Developer Guide - Integrating Amazon Bedrock with an Amazon Kendra index: This guide shows how Kendra can be used as a data source for RAG, confirming it's a valid but higher-level solution. It states, "You can use an Amazon Kendra index as a data source for your knowledge base to build a solution with the retrieval augmented generation (RAG) model." (Source: AWS Documentation, Amazon Kendra Developer Guide, Section: "Integrating Amazon Bedrock with an Amazon Kendra index").

4. Amazon SageMaker Developer Guide - Prepare ML Data with Amazon SageMaker Data Wrangler: "You can use SageMaker Data Wrangler to simplify the process of data preparation and feature engineering..." This confirms its purpose is distinct from generating semantic embeddings for RAG. (Source: AWS Documentation, Amazon SageMaker Developer Guide, Section: "Prepare ML Data with Amazon SageMaker Data Wrangler").